2020/08/27 by Md Abul Bashar, Bashar, Md Abul, Richi Nayak +3
Computer Science · Medicine · Social Sciences · #Crime, Deviance, and Social Control #Data-Driven Disease Surveillance #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2008.12435
openalex publication_date 2020/08/27 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
When people notice something unusual, they discuss it on social media. They\nleave traces of their emotions via text expressions. A systematic collection,\nanalysis, and interpretation of social media data across time and space can\ngive insights on local outbreaks, mental health, and social issues. Such timely\ninsights can help in developing strategies and resources with an appropriate\nand efficient response. This study analysed a large Spatio-temporal tweet\ndataset of the Australian sphere related to COVID19. The methodology included a\nvolume analysis, dynamic topic modelling, sentiment detection, and semantic\nbrand score to obtain an insight on the COVID19 pandemic outbreak and public\ndiscussion in different states and cities of Australia over time. The obtained\ninsights are compared with independently observed phenomena such as government\nreported instances.\n